How do you use ThingSpeak?

Some people use ThingSpeak for monitoring machine processes. This allows them to share the data with potential customers and ensure the process is within control. There are over a million ThingSpeak channels representing a vast assortment of projects.  Some projects measure the temperature and humidity in one room, some projects include a global network of air quality monitors. You can send data to ThingSpeak from your devices, create instant visualization of live data, and send alerts.

What type of data is accepted by ThingSpeak?

ThingSpeak accepts strings, integers, decimal degrees, and any encoded data. ThingSpeak stores data as strings of up to 255 characters per field. ThingSpeak organizes data by channels, where a channel represents data from a given device or process. Each channel contains eight data fields, three fields for location: latitude, longitude, and elevation, and one extra field for a status report.  If you write a number into a field (integer or float), ThingSpeak will display the numbers in field charts on your channel view.

Is ThingSpeak free?

Yes, for non-commercial use. ThingSpeak has different license types for different intended uses. The free license has some restrictions. Purchasing a paid license allows a faster update rate and the creation of additional channels.  For more info, see How to Buy and the ThingSpeak FAQ.

How do you collect data from ThingSpeak?

Once your data is stored in ThingSpeak, you can retrieve your data from ThingSpeak from MATLAB, a REST API, or MQTT API.

Many devices can take advantage of the ThingSpeak library for Arduino and Particle devices. You can use the address bar in your web browser to test updating a channel via the REST API.

Use this format to update your field.

https://api.thingspeak.com/update?api_key=<YOUR_API KEY>&field1=<YOUR_VALUE>

If you have any questions for Christopher, myself, or the rest of the community, ask them at the ThingSpeak Community site.

Thanks to all of our users who keep collecting data, adding devices, and analyzing data on ThingSpeak! This is a huge milestone for all of us and you are making an impact on IoT all around the world.

To commemorate one million channels, Christopher saved the number of channels at the beginning of each year to an array and used MATLAB to fit a power function to the data. You can do this using a MATLAB Visualization in ThingSpeak. The fit parameters are a = 1693 and b=2.277 for the model shown here.

According to a Business insider article, there will be 25 billion IoT devices by 2025. If the present rate continues, at least .1% of those devices can be on ThingSpeak by 2024 when we reach 2.5 million channels!

Here’s some MATLAB code to generate this plot and estimate the number of the ThingSpeak channels in the future.

% Gather data
dates=2015:2020;
absYears=dates-2014;
numChan=[22155,75957,208835,394780,666479,1000000];

% Plot the data
plot(dates,numChan,'r*-','linewidth',3);
xlabel('time');ylabel('Number of Channels');title('1 Million Channels!');
[xData, yData] = prepareCurveData( absYears, numChan );

% Set up fittype and options
ft = fittype( 'power1' );
opts = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts.Display = 'Off';

% Fit model to data
[fitresult, gof] = fit( xData, yData, ft, opts );
hold;
extendRange = 2015:2025;
plot(extendRange,fitresult(extendRange-2014),'r--');
yline(2.5e6,'b','LineWidth',3);

We wanted to thank you again. We look forward to the next million channels and supporting your IoT journey. Let us know in the comments what you are doing or planning to do with ThingSpeak and what functionality that will help you along the way.

My process of smoking meat, cheese, or even ice cream is to monitor only the meat temperature and the internal temperature of the smoker. When I finish a cook, I go back and try to learn from the data. I do not attempt to control the smoker using the Internet of Things, I use IoT to collect the data with ThingSpeak, analyze the data with MATLAB, and apply the insights to the next cook. The best advice that I have been given is to not change too many variables. Stick with simple rubs, the same charcoal, the same wood, the same cuts of meat, etc. Only change one variable for one cook. It takes years of trial and error before you get great at BBQ.

With the help of ThingSpeak and MATLAB, I can help you understand one of the more frustrating parts of smoking meats… the stall! The stall is a period of time where what you are cooking does not seem to be increasing in internal temperature. I used to panic during the stall and mess with the temperature by adding more charcoal or turning the vents. Overtime, I realized this is normal. No reason to panic.

Cooking is taking something cold and getting its internal temperature up to a safe level. Everything that you cook has a different target internal temperature, but generally the sweet spot is 190-205F. Smoking is about low and slow cooking. This means that you are trying to raise that internal temperature slowly with a low temperature. I use a cook temperature of 230-250F. This means that smoking takes a lot of time to properly cook. In some cases, this is two hours per pound. Things don’t change much minute to minute in a BBQ pit, so you can collect data every few minutes and still have an accurate picture of what’s happening.

Collecting Data

First, I set up a ThingSpeak channel to store my temperature data. One field is for the smoker’s temperature and field two is for the internal temperature of what I am cooking. To get the data, you have many options. I did a quick search around the internet and discovered hundreds of Arduino and ThingSpeak projects to get data from a smoker. In general you need two temperature probes that can take the heat of the smoker, connectors, an Arduino with Wi-Fi like the MKR1000.

Analyzing the Temperature Data

The first step is to read in the temperature data using MATLAB that was collected by ThingSpeak. Data on ThingSpeak is stored in a channel and identified by a channelID. If you have a private channel, you will need to supply a Read API Key to get access to the data.

smokerTT = thingSpeakRead(279400,'ReadKey','9AYZQDT1XFDM98FW','OutputFormat','timetable','NumPoints',115);

Sometimes the data from your temperature probe is noisey. This means the value that the temperature probe reports is sometimes higher or lower than the actual temperature. If you take a few samples and take a median, you get a consistent result. In MATLAB, I use smoothdata to take a moving median of my time-series sensor data.

smoothSmoker = smoothdata(smokerTT);

After I clean up the data, I want to visualize it and to see what happened. This is a good time to learn how long things actually take, so on your next cook you budget the right amount of time and don’t rush any phases.

plot(smoothSmoker.Timestamps, smoothSmoker.Variables);

I can use this data to determine how long the total cook will last and even set alerts using ThingSpeak. I tend to watch the smoker and tend the fire.

Results

After a lot of research, I discovered what the stall is doing and why it is important to keep your patience and push forward. This is starting to sound like a metaphor for life. According to Prof. Greg Blonder, “The stall is evaporative cooling”. Prof. Greg is a physicist, entrepreneur, former Chief Technical Advisor at AT&T’s legendary Bell Labs, and regularly contributes to AmazingRibs.com. He likes to bust myths about barbequing and help us understand the thermodynamics of cooking. You are heating the meat slowly, but the moisuture in the meat is evapotaring at the same rate. This effect causes a stall in the temperature rise. The temperature of the meat will go up when the moisture is depleted. The stall is important to the cooking process and leads to something called the bark. This is an outer layer of the meat that is slightly thicker and less tender than the rest of the meat, but traps in lots of flavor and adds to its complexity. So, don’t rush during the stall. Keep the smoker temperature constant – avoid the temptation to turn the heat up to get through faster. And in the end, enjoy some well made BBQ with friends and family over the summer weekends and holidays.

With multiple sensors around my house or office, I want to be able to send data to multiple ThingSpeak channels. But, when I want to perform data analysis, I have a hard time working with data from multiple channels. The channels do not have the same time stamps and are out-of-sync with each other.

With R2016b of MATLAB^®, I am able to use the new timetable data container. Once the data is a stored as a timetable, I can perform powerful operations such as retime, synchronize, and rmmissing.

In this example, I have two sensors outside of my office here in Natick, MA. One sensor is a temperature sensor that is sending data to ThingSpeak channel 163540. My other sensor is writing humidity data to channel 163545. Both channels are public. My goal is to plot temperature versus humidity over one time series. To accomplish this, I will use timetable and synchronize inside of a new MATLAB Visualization on ThingSpeak.

% Read from the temperature channel
temperatureTT = thingSpeakRead(163540,'Fields',1,'NumPoints',100,'outputFormat','timetable');

% Read from the humidity channel
humidityTT = thingSpeakRead(163545,'Fields',1,'NumPoints',100,'outputFormat','timetable');

% Synchronize two timestables and fill in missing data using linear interpolation
TT = synchronize(temperatureTT,humidityTT,'union','linear')

% Plot Temperature and Humidity over time
plotyy(TT.Timestamps,TT.Temperature,...
       TT.Timestamps,TT.Humidity);
        
title('Temperature and Humidity Synchronized From Two Channels')
xlabel('Temperature and Humidity in Natick, MA')
legend('Temperature','Humidity')

The first part of the script reads in ThingSpeak data from two different channels and stores the data in two timetables. Once the data is stored in a timetable, I am able to take advantage of synchronize. With synchronize, I can combine both timetables with one time series and fill in missing data using linear interpolation. This results in a plot that shows my data over time without any missing data. To create the plot, I signed into ThingSpeak, selected Apps, and created a new MATLAB Visualization with my MATLAB code.

All ThingSpeak users are able to try this example or explore the other new MATLAB features directly on ThingSpeak. I will leave my temperature (163540) and humidity (163545) channels public, so you can try out timetable example without having to connect devices to ThingSpeak.

We write the temperature and humidity values from the weather station to a ThingSpeak channel. At some point in the project, we started to wonder about dew point calculations. We wrote some MATLAB code that combined the temperature and humidity to calculate dew point. I did this using the ThingSpeak app, “MATLAB Analysis”. You can try this out with ThingSpeak now by signing in, selecting Apps, MATLAB Analysis, New, selecting “Calculate Dew point”, and clicking “Create”. This happens to be one of our built-in examples using our weather station’s public data.

It is great that it was easy to calculate dew point with MATLAB, but I want to see this analyzed data over time just like any other sensor data. The solution is a powerful combination of MATLAB Analysis and TimeControl. We use MATLAB Analysis to do the analysis and write the data to a ThingSpeak channel. Then, we use the TimeControl app to repeat the analysis every 5 minutes.

To setup MATLAB Analysis on a schedule, sign into ThingSpeak, select Apps, TimeControl, and New TimeControl.

My MATLAB code now runs every 5 minutes doing analysis and writing data to my ThingSpeak channel. The TimeControl settings can be tailored to your needs such as executing MATLAB code once a day or only on weekends. This combination of MATLAB Analysis + TimeControl allows you to create continuous analysis of your project data.

To try this out for yourself, we have a public channel of weather station data that we have collected in Natick, MA at our headquarters. You can use that data and do your own MATLAB Analysis and writing the results back to your own channel. Also, Check out the ThingSpeak Documentation where we have a complete tutorial for you to help get started with ThingSpeak and MATLAB.

Everything you need to know in order to build your own sensor logging project is on Noel’s Instructables.

[via Instructables]

All of the source code to connect Electric Imp to ThingSpeak and the 3D printer design files are available on Marcus’ blog ‘slickstreamer‘.

[slickstreamer / Dangerous Prototypes]

Check out the tutorial, “ThingSpeak Temperature with Raspberry Pi“, to learn how to send sensor data using the BrickPi, a Raspberry Pi computer, and a temperature sensor for the LEGO^® MINDSTORMS^® NXT. The project uses ThingSpeak to store, share, and visualize sensor data collected by BrickPi-enabled projects. The Python code for the Raspberry Pi is available on GitHub and the entire project is open source!

[via  Dexter Industries]

Looking over the code for the Electric Imp, it looks pretty easy to cross-clouds from the Imp to ThingSpeak. Check out the source code on GitHub and full details on Slickstreamer.

[via Slickstreamer]

For more information and Python source code, visit MY NERD JOURNAL.

[via MY NERD JOURNAL]

Indoor Environmental Quality (IEQ) is the measure of comfort and includes factors such as temperature, humidity, pressure, noise level, and indoor air quality. don’s original goal was to monitor the IEQ of his baby’s room, but he soon realized that his project has more applications around the house and for others.

Don published the full details of the project and submitted it to the Green Design Contest at Instructables. Great work!

[via Instructables]

Here’s what the temperature looks like now in Australia:

Check out the family’s blog for the source code and to learn how to create your own solar water heater monitoring system.

[via Klink Family Adventures]

This video that we discovered on YouTube is the team’s presentation. You will get to see ThingSpeak in action, live in front of an audience about halfway thru…

We hope you got an “A” on the project (do they still give letter grades?)!

The Teracom box allows for 1-wire connections to sensors. David connected a temperature sensor to the 1-wire bus, an Ethernet connection, and customized the controller to push data to ThingSpeak for data logging of environmental sensor data. The tutorial also includes great photos clearly showing the setup for others to repeat.

Behind the scenes, Lars uses the Arduino microcontroller to collect data from the sensors and uses Processing to publish data to his ThingSpeak Channel.

From Lars’ project site:

The goal of this project is to log some weather data and be able to access it from anywhere. There is some sensor data (temperature, relative humidity, pressure, and ambient light) and some computed data (dew point). You can see the weather condition in my apartment in Ithaca, NY at my ThingSpeak Channel 346. You can also look at the Google Chart of my own MySQL solution, which I no longer maintain.

Check out a detailed breakdown of the Weather Station project and more awesome projects on Lars’ project site, called “make.larsi.org“.

Visit the ThingSpeak Plugin page for more information on how to use this plugin with your HomeVision home automation system.

[via MyBlog4Fun.com]

Frank says,

This project uses a mbed microcontroller (LPC1768 ARM Cortex-M3) to monitor temperature using a DS1620 (digital temperature sensor IC), retrieve the time via NTP (network time protocol), and then log the current temperature to ThingSpeak along with a time-stamp.

[via Circle of Current / Dangerous Prototypes]

I have been playing around with the ThingSpeak API a little this week. I decided to get my FEZ Cobra reporting temperature data from my office at work. Why a temperature sensor you ask?… Because I had one laying around.. That was good enough for me, guess I’m simple like that.

[via codefox blog]